Paper Title:
Optimization of Parameters for CO2-Gas Shielding Arc Welding Based on the Double Weights Neural Network
  Abstract

It is very difficult to predict the process detection and performance parameters, because of the highly nonlinear and multivariable coupling of welding process. In this paper, we construct a new method in optimization of parameters for CO2-gas shielding arc welding. By using the new Double Weights Model, this algorithm can give the Direction Weight, also the Core Weight at the same time. The new network inherits the traditional BP network and RBF (Radial Basis Functions) network with multivariable parameter settings and so on. We apply the network to the optimization of welding parameters, experimental results show that this algorithm can use less generations to calculate and get more accurate optimization effects, also not serious about a local minimum compared with RBF while using the same environment and equal network scale. Experiment proves that it is feasible to control welding parameters by the Double Weights Neural Network.

  Info
Periodical
Edited by
Jerry Tian
Pages
175-179
DOI
10.4028/www.scientific.net/AMR.304.175
Citation
L. L. Lv, J. P. Gu, L. Hua, W. Fan, Y. J. Qiang, "Optimization of Parameters for CO2-Gas Shielding Arc Welding Based on the Double Weights Neural Network", Advanced Materials Research, Vol. 304, pp. 175-179, 2011
Online since
July 2011
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